# Construct the parameter set from the organism file specified in config
ps = params.parameter_set_from_file(input_dir + "/" + organism_file)
# Construct the landscape from the landscape file specified in config
landscape = lscp.landscape_from_file(input_dir + "/" + landscape_file)
ps.landscape_size = landscape.shape
# If a single run is being performed
if args.task == "run":
ps.convert_to_age_classes()
Ns = run_parrallel_iterations(ps, landscape, config)
elif args.task == "run_trajectories":
# Read in the trajectories from file, if ext is set, the original trajectory file has been split into multiple
# files and each trajectory has been assigned a working sub-directory under the output_directory
if args.ext == "":
ts, ptv, _ = io.read_all_trajectories(input_dir + "/" + trajectory_file)
else:
output_dir += "/Trajectory" + args.ext
ts, ptv, _ = io.read_all_trajectories(output_dir + "/" + trajectory_file + "." + args.ext)
if args.remote:
run_trajectories_remotely(ts, num_processors, walltime, memory, output_dir, trajectory_file, config_file)
io.cat_trajectories(len(ts), ptv, ps.sentinels, output_dir)
else:
# If a specific trajectory is to be run
if args.trajectory > 0:
ts = [run_trajectory(ts[int(args.trajectory)], ps, landscape, ptv, num_iterations, pool)]
else:
ts = [run_trajectory(t, ps, landscape, ptv, num_iterations, pool) for t in ts]
io.write_trajectories(ts, ptv, ps.sentinels, output_dir + "/results.txt")
x[index,:] = point.independents()
if np.all(x[index, :] == x[index - 1,:]):
x[index, :] += x[index, :] / 1000.
y[index, :] = y[index - 1, :]
else:
y[index,:] = point.mean()
return x, y
if __name__ == "__main__" :
parser = ap.ArgumentParser()
parser.add_argument('--config', default="config.py")
parser.add_argument('--task', required=True)
parser.add_argument('--design', default="oat")
args = parser.parse_args()
#Load the configuration file
config = os.path.realpath(args.config)
execfile(args.config)
if "generate" == args.task:
ps = pms.parameter_set_from_file(input_dir + "/" + organism_file)
ts = _generate_trajectories(ps, r, levels, jumps, perc, ptv, args.design)
io.write_trajectories(ts, ptv, [], output_dir + "/" + trajectory_file)
elif "analyse":
ts, ptv, sentinels = io.read_all_trajectories(output_dir + "/" + results_file)
ees = _calculate_elementary_effects(ts, ptv, "oat")
io.plot_ees(ees, ptv, output_dir)
#Construct the parameter set from the organism file specified in config ps = params.parameter_set_from_file(input_dir + "/" + organism_file) #Construct the landscape from the landscape file specified in config landscape = lscp.landscape_from_file(input_dir + "/" + landscape_file) ps.landscape_size = landscape.shape #If a single run is being performed if args.task == "run": ps.convert_to_age_classes() Ns = run_parrallel_iterations(ps, landscape, config) elif args.task == "run_trajectories": #Read in the trajectories from file, if ext is set, the original trajectory file has been split into multiple #files and each trajectory has been assigned a working sub-directory under the output_directory if args.ext == "": ts, ptv, _ = io.read_all_trajectories(input_dir + "/" + trajectory_file) else: output_dir += "/Trajectory" + args.ext ts, ptv, _ = io.read_all_trajectories(output_dir + "/" + trajectory_file + "." + args.ext) if args.remote: run_trajectories_remotely( ts, num_processors, walltime, memory, output_dir, trajectory_file, config_file) io.cat_trajectories(len(ts), ptv, ps.sentinels, output_dir) else: #If a specific trajectory is to be run if args.trajectory > 0: ts = [run_trajectory(ts[int(args.trajectory)], ps, landscape, ptv, num_iterations, pool)] else: ts = [run_trajectory(t, ps, landscape, ptv, num_iterations, pool) for t in ts] io.write_trajectories(ts, ptv, ps.sentinels, output_dir + "/results.txt")
